Ankush Banik

I’m a Data Engineer transitioning to Business Intelligence Engineering, with 4 years of experience building and improving large-scale data systems. I focus on ETL pipeline development, cloud data infrastructure, and dimensional data modeling to help teams move faster with trustworthy data.

At Deloitte USI, I designed and deployed end-to-end ETL pipelines using Python and Apache Spark, improving data throughput by 30% for AI tool clients. I also collaborated on scalable data lake solutions and logical/physical data models on AWS S3 and Azure Data Lake for datasets exceeding 10TB.

I’ve consistently driven performance and reliability improvements by optimizing PySpark jobs and Spark queries, reducing job execution time by 40%. I automated ingestion and workflows with AWS Glue, Lambda, and Azure Data Factory, cutting manual intervention by 60%, while enforcing data quality checks with 99% accuracy across Parquet and ORC datasets.

Previously at Carelon Global solution, I analyzed and optimized ETL pipelines—reducing processing time by 25%—and delivered data outcomes that supported business goals (including decreasing default rates by 15%). I’m now eager to contribute to Amazon Prime’s data ecosystem with data-driven decision-making at global scale.